Pharmaceutical companies are in a period of great development and transformation through information technology and data science. Traditionally, pharmacovigilance processes evolved out of data that came from clinical trials and regulatory submissions, open for quite a while now to involve new dimensions with real-world data.
Real-world data (RWD) is a collection of insights derived from everyday clinical settings, patient interactions, and broader population studies. Unlike traditional clinical trial data confined by controlled environments and predefined protocols, RWD offers a holistic view of how drugs perform in diverse, real-life scenarios. This evolution can prove to redefine how pharmaceutical manufacturers, healthcare providers, and regulators ensure drug safety.
Role of Real-World Data in Modern Pharmacovigilance
- RWD Expands Drug Safety Monitoring
The methods of pharmacovigilance relied heavily on voluntary adverse event reporting systems and controlled clinical trial data. While valuable, those techniques are limited in scope and often miss rare or long-term side effects that come out only once a drug is in widespread use. RWD fills this gap by bringing a richer and broader data set that can capture adverse events across a much wider range of patient populations, settings, and conditions.
However, RWD encompasses a wide variety of sources, including Electronic Health Records (EHRs), Insurance Claims Data, social media, etc. When these data sources are integrated, pharmaceutical manufacturers obtain much more accurate, real-time, and holistic safety profiles, opening the possibility for truly proactive pharmacovigilance efforts.
- Enhanced Signal Detection and Post-Market Surveillance
RWD enables the detection of safety signals—patterns that indicate a potential ADR—faster and more comprehensively than traditional methods. The FDA Sentinel Initiative, which collects and analyzes data from distributed networks of EHRs, insurance claims, and patient registries, is an excellent example of how RWD improves post-market surveillance. By analyzing these diverse datasets, regulators can more quickly identify emerging safety issues and respond more rapidly to ensure patient safety.
- Personalized and Real-Time Monitoring
The rise of AI-powered mobile applications and wearable devices has added a new dimension to pharmacovigilance. These technologies enable real-time monitoring and patient-reported outcomes, capturing ADRs as they occur. For instance, apps like MedWatcher and platforms such as the Hugo platform provide patients and healthcare professionals with user-friendly tools to report adverse events, integrating these real-time inputs into broader safety monitoring efforts.
Regulatory Momentum
While the industry is adapting to the increasing reliance on RWD, regulatory bodies have been leading the way in the incorporation of RWD into the safety monitoring of drugs. A number of diverse and key events indicate the commitment of agencies, such as the U.S. Food and Drug Administration or the European Medicines Agency, to install RWD in complementary avenues to benefit drug safety.
FDA’s Leadership in RWD Integration
The FDA has demonstrated a strong commitment to using RWD for pharmacovigilance. The FDA Amendments Act of 2007 called for the establishment of the Active Postmarked Risk Identification and Analysis (ARIA) system, which specifically recognized the value of RWD. The Sentinel Initiative commenced in 2008 and provided a national electronic system for monitoring the safety of FDA-regulated medical products. This initiative enables rapid analysis of such RWD from a variety of sources in real-time, thus enhancing the FDA’s capability to identify safety concerns.
The FDA’s Real-World Evidence Data Enterprise focuses on integrating longitudinal EHR data with claims data. This initiative allows analyses that could not have otherwise been addressed solely with claims data. Additionally, TreeScan is an AI-based tool used by the FDA employed to detect safety signals and validate the use of RWE, leading to the reinforced expectation from the FDA that Marketing Authorization Holders should begin incorporating Real-World Data into their pharmacovigilance systems.
EMA’s DARWIN Initiative
The EMA also has a framework called Data Analysis and Real-World Interrogation Network (DARWIN) initiative, which aims to harness RWD to support regulatory decisions across Europe. Currently, DARWIN holds RWD for 130 million patients, contributed by 40 data partners. This initiative is pivotal in ensuring that the regulatory process remains informed by real-world insights, particularly for post-marketing drug safety monitoring.
Democratization of AI
In parallel with regulatory momentum, the democratization of AI is revolutionizing how pharmaceutical companies approach AI technologies, particularly natural language processing (NLP) to analyze large volumes of unstructured data, such as clinical notes, patient reports, and social media posts.
- AI Milestones: BERT, GPT, and Beyond
The release of models like BERT (Bidirectional Encoder Representations from Transformers) in 2018 and ChatGPT in 2020 has dramatically improved the efficiency of data extraction and analysis in pharmacovigilance. These models excel at understanding context in natural language, enabling AI systems to process vast amounts of unstructured data and identify ADR signals more effectively.
For example, ChatGPT’s ability to interact with users in real-time could make accessing crucial safety information more accessible, helping pharmaceutical professionals make faster, data-driven decisions.
- Lowering Barriers to Entry
The rise of AI-powered tools has lowered the barriers to entry for organizations of all sizes. Smaller pharmaceutical companies that were once unable to afford the significant investment required for advanced AI systems, can now leverage AI-powered automation tools to build custom models for their specific needs. This democratization allows organizations to efficiently monitor RWD and detect ADRs, reducing reliance on traditional methods and freeing up human resources to focus on more complex safety evaluations.
How AI and RWD Are Interconnected
The ability of AI to bring pharmacovigilance research into balance depends on the quality of the data, in other words, its ability to service the data sufficiently deep for processing. While dissimilar RWD serve as the grounding major for more powerfully built AI systems that dispense deeper insights, many such AI built systems will have limited scope of application-very robust ones within the pharmaceutical industry among major causes contributing to a shrinkage of intelligence in companies that maintain distance. The use of AI, when integrated with RWD, enables organizations to realize the best execution of actionable insights in drug safety monitoring and regulatory compliance.
Conclusion
With regulatory authorities prioritizing real-world data (RWD), the time frame for marketing authorization holders to reap the benefits of RWD implementation, coupled with the ease of AI capability implementations, is rapidly shrinking. Those who embrace this change will be best placed to ensure drug safety, compliance, and patient trust for the future.
DDReg can assist pharmaceutical companies to navigate these changes, ensuring both compliance and innovation. Contact us today to discover how we can assist you in leveraging these transformative technologies. Read more from DDReg here: A Complete Guide to Regulatory Pathways for Biosimilars in the EU and US